This section is intended to introduce various aspects of the art, which may be associated with exemplary embodiments of the present invention. This discussion is believed to assist in providing a framework to facilitate a better understanding of particular aspects of the present invention. Accordingly, it should be understood that this section should be read in this light, and not necessarily as admissions of prior art.
Oil and gas fields are often metered with a wide array of measuring devices, and engineers routinely monitor production volume flow rates and other data in real time or with a short delay. Engineers monitor these measurements in order to assist in making decisions about field operations. Adverse events may severely impact the production performance of the oil and gas wells, and the accurate and timely identification of these events are the engineer's responsibility. If such events are identified in an accurate and timely manner, it is often the case that remedial action can be taken which will lower production cost, reduce downtime, improve hydrocarbon recovery, and/or increase well productivity. Avoiding adverse well production events is an important part of meeting the world's energy needs.
High quality and high frequency well production data is a recent development, but the technology for processing and interpreting such data remains in a nascent state. Because of the high frequency of the data, which gives rise to large data sets, there is a need to prioritize the order in which the data is analyzed for wellbore events. Current methods are not timely, they lack a robust prioritization method, and are prone to inaccuracy.
Presently, the data are viewed using automated charting tools drawing on a production database. Engineers use their experience to discern wellbore related events from these time-series measurements, and then use their professional judgment to decide what action to take. For example, an engineer might see rapid variation in production volumes in combination with temperature variations on the time scale of hours. Should this pattern repeat several times, an experienced engineer suspects that the well may have liquid loading issues, and might pick up the phone to call a field engineer, asking them to investigate and potentially change the flow control setting (e.g. choke) to alleviate the variations.
In current practice, the data are viewed using automated charting tools drawing on a production database, and a basic automated methodology is used to flag events for follow up by an engineer at a later time. The idea is to process the measured signals, searching for measurements which indicate a deviation from a normal state—a possible well event. For example, Oberinkler, et al. (SPE #87008) suggests using a detection system where a threshold is placed on the water cut of a well. If the water cut rises above the set threshold, then the well has probably experienced permanent water breakthrough, and an alarm is sent to the engineer. Hooimeijer, et al. (SPE #104161) have described this general framework for production event detection and call it surveillance by exception. Other authors have described similar methods for the detection of various wellbore-related fluid production phenomena. Van Zandvoord, et al. (SPE #100342) have developed an event detection and alarming system for wells produced with electric submersible pumps as well as wells that are produced using gas lift. Poulisse, et al. (SPE #99963) describe detecting water coning and water breakthrough, two phenomena afflicting subterranean wells. Lentini, et. al. (SPE #102139) used tubing head pressure, flowline pressure, and pump amperage measurements as the basis for detecting gas-lock, low production, and slugging, events that affect electric submersible pumps. The detection scheme used basic trend determination and threshold counters. Kosmala, et al. (U.S. Pat. App. No. 2007/0175633) have described a typical method for the identification of events affecting electric submersible pumps (one of many possible production methodologies for an oil well) installed in wellbores. The methodology consists of specific steps for measuring, outputting, transmitting, and processing signals related to pump performance.
All of the methods described above are threshold based methods. In this methodology a single-value threshold or an operating range defined by two values is used to establish a hard cutoff to trigger an alarm. Deficiencies of this methodology are an overabundance of false alarms, large time requirements for setting the thresholds well-by-well, and alarms with no confidence limits.
The apparatus and methodologies used for adverse event detection during drilling operations are often more advanced than those used in production operations. For example, Jervis, et al. (U.S. Pat. No. 5,952,569) have described a method and apparatus for the identification of adverse drilling events that include well kick, formation fluid influx, stuck drill pipe, pipe washouts, and other drilling events. The method relies upon measurements from flowmeter paddles, electrochemical transducers, measurement while drilling parameter sensors, and mud tank volume sensors. Their method relies upon the construction of mathematical derivatives (time derivatives, sums, products, etc.) of these measurements, their comparison to a database of prior knowledge (encapsulated in a Bayesian network) of what the mathematical derivatives of these measurements look like when an adverse event is occurring, and a probabilistic estimation of the likelihood of that specific adverse event based on any differences between the compared signal derivatives. Zheng, et al. (DOE/ID/13681-2) and Dunlop, et al. (U.S. Pat. No. 7,128,167) have also suggested using Bayesian networks to detect well kick and other drilling events. Niedermayr, et al (U.S. Pat. No. 6,820,702) have described a method and apparatus for the detection of specific drilling events (stuck pipe, pack off, or kicks), and suggest the use of a neural network or fuzzy logic processor, methodologies that are more advanced than a simple threshold based method. McDonald, et al. (U.S. Pat. No. 6,732,052) have also suggested the use of a neural network to detect drilling events.
Historically, Bayesian networks have been used in non-real-time applications and have relied largely upon expert knowledge and not statistical learning in their construction. For example, Woronow, et al. (WO 2006/112864) have used Bayesian networks to predict sand quality in geological formations. Only in drilling applications have others seen utility in real-time applications of Bayesian networks so far.